AI Can Solve the Wrong Problem Perfectly Raising the Barr Weekly Memo: Issue 714

For years, I have said that a brilliant answer to the wrong question is still worthless.

That was true long before AI. It matters even more now because AI can produce an answer so quickly, confidently, and professionally that we may never stop to ask whether it solved the right problem.

A weak prompt can produce a meaningless answer. A poorly defined business problem can produce something more dangerous: a polished skill that repeatedly solves the wrong thing.

The Question Behind the Challenge

Recently, I asked members of the AI Insider Lab to send me one difficult business challenge that keeps returning. I was specific about what qualified. It could not be a one-time decision. It needed to be something they had confronted repeatedly, something worth turning into a skill they could run whenever the problem returned.

I gave them a comparison. “I need to get better at preparing for client meetings” was too vague. A useful version identified the recurring situation, the information gathered, the time consumed, and the package rebuilt from scratch before every first meeting with a prospect.

The responses I received led me to a larger question: How many experienced professionals can clearly identify the recurring work they perform well enough to teach it to AI?

We often recognize the frustration without recognizing the pattern. Every client appears different. Every proposal feels customized. Every strategic decision seems to depend on circumstances that did not exist last time. Yet underneath those differences, we may be performing the same diagnosis, gathering the same evidence, testing the same assumptions, and rebuilding the same reasoning over and over.

The Skill Comes After the Problem

I did not begin by deciding to build a skill called AI Weekly Monitor. I began with a recurring problem: too much AI news and too little distinction between noise and developments that could materially affect my clients, my business, or the AI Insider Lab.

The Article Topic Radar skill began the same way. I needed a reliable process for identifying timely article opportunities while avoiding arguments I had already explored across fourteen years of publishing.

In both cases, the skill came after the problem was understood. Before writing instructions, I needed to define the scope, identify the necessary sources, establish the standards, and determine what a successful result should contain.

That is the part many people skip. They begin writing instructions before they have diagnosed the work. AI then follows those instructions and produces something that may look excellent. But excellent execution does not rescue a faulty definition.

The First Version Is Not the Finished Skill

Neither skill was complete when I first created it. The initial version was a working hypothesis about how the problem should be solved.

Every time I ran it, the core problem remained the same, but real use exposed conditions no initial design could fully anticipate: a source that needed greater authority, a standard that required more precision, an exception that needed handling, or a validation step that needed strengthening. I refined the skill accordingly. Its purpose did not change. Its ability to fulfill that purpose became considerably stronger through repeated use.

That does not mean adding every new idea to the same skill. If a newly discovered challenge belongs to the skill’s original purpose, I refine the skill. If it represents a different job or outcome, it deserves a separate skill. Otherwise, improvement becomes expansion, and the skill eventually becomes bloated, confused, and unreliable.

Ask AI to Diagnose Before It Solves

AI may discover that you have defined the wrong problem, but it is unlikely to do so automatically. Most of the time, it assumes that the problem you presented is the one you want solved. It tries to be helpful by proceeding.

Here is a prompt worth testing before asking AI to solve anything:

The sequence matters:

• Ask the wrong question, and even a brilliant answer is worthless.
• Define the wrong problem, and the skill repeats the mistake.
• Give that skill more autonomy, and you automate the mistake at scale.

From Skills to Coordinated Work

Once my AI Weekly Monitor and Article Topic Radar skills became reliable, I scheduled them to work in sequence. The first gathers and evaluates consequential developments. The second uses that intelligence, along with my publishing archive, to identify stronger article opportunities.

That is the beginning of agentic work. A skill knows how to perform a defined piece of work. An agentic workflow determines when that work should happen, coordinates it with other work, and moves toward a larger outcome.

But the technology is not the starting point. The starting point is seeing the work clearly enough to define it.

Before You Build the Skill

Before rolling up your sleeves, ask yourself:

• What problem keeps returning?
• What am I actually doing each time it appears?
• What information and judgment does the work require?
• What would a successful outcome look like?

If you cannot answer those questions, do not build the skill yet. Stay with the diagnosis.

AI can solve the wrong problem perfectly. Your responsibility is to define the problem before asking AI to solve it.

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AI Can Solve the Wrong Problem Perfectly